c l i c k  m e

c l i c k  m e

On each of your computers, on the C: drive, is a folder called Delaware Data.

These are Delaware County data sets, some updated by the county as recently as the last few weeks.  You will use these data layers for the mid-term evaluation and course project.

Don’t remove any Delaware Data files from the computers!  I do have this data backed up on a OWU networked drive.

Note: due to a network problem, the Delaware Data is not on computers 4 and 19.  InfoServices is working on the problem and hopefully it will be resolved soon.

In order to familiarize yourselves with the data, please do the following by Wednesday March 4:

• Make a new blog entry and title it Delaware County Data Review. Categorize it as an Exercise (so I can find it).
• In the blog entry, indicate the name of each Delaware map layer. Write a sentence describing each map layer in the blog entry.  If there are multiple map layers in a folder, document each.  Again, the point is to familiarize yourself with the Delaware GIS data for the mid-term and your project.
• If you are confused by a data set, indicate that in your comments.  If the data set is not described, search the web and see if you can figure out what it is.

To view the map layers:

• Start ArcMap and create a new ArcMap project.  Save it as deldata.mxd in your folder in the Geog 355 folder
• Hit the + button to add map layers to the project
• Navigate to the Delaware Data folder and open the first folder.  Add the available shape (.shp) files (map layer with attributes).  In most cases, but not all, there is only one .shp file in each folder. Take a look at the map and its table of data (attributes).

Note: the two folders of orthophotos contain very large image files.  If ArcMap is slowing down after adding these layers, feel free to remove the layers from your ArcMap project file after you take a look at them.  Please, in your blog entry, document what an orthophoto is.

• In some cases you can easily figure out what the data is – hydro, for example (rivers, etc.).  In other cases you will have to refer to the metadata (data about data) for the Delaware Data at the Delaware County GIS web site (DALIS).  Once there, select Files to Download. Fill in the info at the bottom, check the box, and hit Download Data.
• A page of available datasets appears.  Don’t download any data (that has been done for you) but do check out the metadata link for each data set.  This should clear up basic questions about the data.

Ask me if you are having any problems!

I was moving some piles of junk and came across a 1934 U.S. Public Works Administration book on Mississippi Valley public works projects (Report of the Mississippi Valley Committee of the Public Works Administration, October 1, 1934).  The book is full of maps and other information graphics influenced by Otto Neurath’s picture language, isotype.  Isotype is visually distinctive and activist and populist in intent.

Some examples of the isotype “language” from a 1937 article by Neurath:

It struck me that we could use isotype inspired maps and graphics for our “dis-orientation” guide and poster.  We are copying the idea of the UNC map, but with an entirely different look.  Isotype – with its activist and populist bent – seems to be very appropriate.  And funky-retro.

Further, I think we can roll the majority of class projects into this poster, or at least some part of each of the projects, so we have a green/sustainability oriented dis-orientation map.  Isotype is certainly related to cartography and GIS and the goals and intent of our projects.  Further, isotype design was at its peak during the 1930s – the last great depression.  That corresponds with the current not-so-great semi-depression.

Otto Neurath (1882 – 1945) was a philosopher, sociologist, and political scientist. One of his many concerns was education, and in particular, enhancing the understanding of statistics and other numeric data.  To this end Neurath, Gerd Arntz, and Marie Reidemeister created the pictorial language isotype. A few examples open this posting.

We may want to have the last set of readings / student presentations focus on the ideas behind isotype – thus focusing on the way we plan to present our work to the eager public.

A few interesting isotype & Neurath resources to look at:

The Isotype Institute carries on the tradition of isotype, and includes many isotype graphics to look at.

1930 Atlas of Gesellschaft und Wirtschaft (Society and Economy): big PDF of entire atlas.  Sybilla Nikolow discusses the atlas in her article “Society and Economy: An Atlas in Otto Neurath’s Pictorial Statistics from 1930.” (PDF)

Ellen Lupton reviews the history and significance of isotype in her article “Reading Isotype.” (PDF)

Neurath and the Vienna Method of Picture Statistics (PDF).  A chapter out of an e-book called Speaking of Graphics An Essay on Graphicacy in Science, Technology and Business by Paul J. Lewi.  Seems like a nice overview of the history of isotype and its characteristics.

The DADA Companion has much information on design and art related to isotype.  Search for “isotype” or “Neurath.”

A new book should be out in April of 2009 called The Transformer: Principles of Making Isotype Charts by Marie Neurath and Robin Kinross.  A copy is ordered for our library.

Gerd Arntz Web Archive. A super collection of thousands of isotype symbols designed by Arntz.  All seem to be free to use.  The site also has a breif biography of Arntz.

Austin Kleon’s blog on graphic design has a nice posting on isotype, comics, and information graphics design. Search the blog for other isotype references.

The web magazine Mute has a feature called The Dutch Are Weeping in Four Universal Pictorial Languages At Least that reviews a series of contemporary exhibits that focus on isotype and related ideas.  One exhibit called After Neurath has a significant amount of information and links.

The New York Times summarized 2007 US and Coalition member deaths in Iraq in a isotype-esque chart (click for larger version):

Stroom De Haag writes (in the online magazine Archined) about Neurath as the “grandfather of open source.”

Lots more out there…

Maurizio Cattelan  Love Saves Life  1995

Schuurman: GIS: A Short Introduction

Ch. 4: Bringing it All Together: GIS Analysis

GIS is often used to store data; analysis greatly extends the functionality of  GIS by allowing us to learn more about the stored data

Cadastral systems: property and attribute information (Delaware DALIS project)

• storing data vs analysis (how many residential properties within 1000’ of river)

Examples of analysis:

• measurement & distance calculation (perimeters, areas, line lengths)
• point in polygon queries: does a point lie in an area?
• shape analysis: shape of a line to assess difficulty of driving on a road
• edginess analysis: deer habitat (prefer ediginess, forest grass boundary)
• slope calculation

Overlay Analysis, Set Theory, Map Algebra

• query: a question (show all owl locations > 500’ from road)
• buffer: an area around a pt, line, area (show residences within 500’ of liquor lic. Appl.)
• overlay analysis (find all soils of a particular type within a floodzone)
• difficulty of polygon overlay: extensive calculation
• set theory & map algebra: mathematical basis of GIS analysis

Spatial Analysis in the Field: Environmental Modeling

• ex) modeling industrial pollution
• predict the impact of a new industrial development in a particular location; help
• in decision-making
• air emission, noise, risk
• link environmental modeling to spatial data

Building Intuitive Models: Multi-Criteria Evaluation

• location decision analysis
• find the best location for a new industrial development, given multiple criteria (away
• from people because of pollution; near necessary transportation corridors, labor).
• ex) locating a dump
• factors and criteria: p. 110
• resulting map: worse and better locations: p. 111

The Power of the Eye: Visualization and the New Cartography

• ex) TB example

From Data to Analysis: A Case Study of Population Health

• ex) population health: relating housing to health
• ecological fallacy: aggregation or scaling introduces bias (p. 120)

MCE and Analysis

• example of health vs density of population

Calculation and the Rationalities of GIS

• critical perspectives

Ch. 5: GIS Training and Research

• GIS is slow
• Evolving research in GISci
• Not ontology again!
• Feminism and GIS
• Systems vs Science

Some Resources:

• gis.com (ESRI)
• gislounge.com
• gis job clearinghouse
• gisuser.com
• giscafe.com

On no snow for Christmas in Finland

Schuurman: GIS: A Short Introduction

Ch. 2: GIS, Human Geography, and the Intellectual Territory Between Them

GISystems & GIScience based on assumptions that privilege certain approaches to understanding the world (natural and human).

Geography: diverse, undisciplined discipline, origins in 1800s

GIS since the late 1960s, parts of cartography & quantitative methods

• sometimes rocky relationship between these and Geography in general

Mind the Gap: The Distance Between Human Geography and GIS

Little overlap between GIS and Human Geographers until the late 1980s

Geographers critique: GIS is mere technique, no intellectual component

• GIS processes facts, but can’t generate meaningful understanding
• GIS based on positivism and/or naïve empiricism: neither well respected approaches/theories in Geography

Positivism/empiricism: experiment/test/trial: sense perceptions are the only admissible basis of human knowledge and precise thought; natural and social processes can be understood (via hypothesis testing and data analysis) and follow strict laws; designed to supersede theology and metaphysics.

• ex) central place theory in Geography
• ex) much of science and some social science.
• ex) less comfort with qualitative methods
• ex) less comfort with theories that use empirical data but don’t see laws governing human behavior and activity (feminism: role of gender in shaping society, but these are not laws – they can be overcome and changed for the better of all)

1980s: lots of debates

• GIS people with a more positivistic, scientific approach vs human geographers with more qualitative, social theory approaches
• GIS very limited view of the world, requires very specific, empirical data, can ask very specific questions, and get very specific results.
• GIS driven by corporate and military needs
• GIS expensive and exclusive; elitist

Brian Harley (The Nature of Maps), Denis Wood (The Power of Maps)

• critique of maps: social constructions for creating and maintaining power
• selectively show certain things, not others; create and enforce social status quo
• maps create a space of political territories (broader scale) and privately owned property (detailed scale) and make those human conceptions real in the landscape
• ex) the “nation” / “states” – relatively new concept; problem in Mid East
• ex) property ownership: relatively new human concept
• ex) zoning

Maps created by elites to shape and enforce geographic reality to suit their needs

John Pickles: Ground Truth: apply same critique to GIS

• GIS is for maintaining order, just like paper maps before them
• Friday Harbor meeting: beginning of a dialog
• alternatives: Particapatory GIS, counter-mapping, qualitative methods “GIS2”

Epistemology and Ontology in GIS

• epistemology: the methods we use to study the world; each has assumptions and perspectives that shape the questions, analysis, and interpretation of results
• ontology: what things really are (how the world must be to make sense of it)
• ontology (computer science)

ex) we have extensive GIS technology for determining the fastest route for an ambulance to get a sick person to the hospital, but we don’t ask why so many people get sick.

ex) GIS is used extensively to plan new developments and roads but is very much less able to help understand the extensive negative impact of such development on the environment

ex) Forest in India

Use quantitative methods to test IR energy reflectance from various types of vegetation and different kinds of land cover in an area; gather data, test hypothesis, generate specific measurable value that differentiates a forest from other areas.

• Use remote sensing to define areas that have a certain % reflection of IR energy; any less than that % is not a forest, any more than that % is.
• then create a map of forests (and not forests; can do this all in GIS from afar)
• Empirical epistemology, we can “sense” reflected energy and use that do define and distinguish forest from other areas.
• Empirical ontology: the world consists of measurable objects, some of which reflect energy and specific kinds of energy reflectance lead us to understand and locate real forests.

Use qualitative methods such as interviews and mental mapping to have different people in the same area of India show where forest is on a map, and describe what forest is to them

• state foresters: will claim much more territory is in forest as it is their job to preserve and create forested areas
• farmers: will identify tree covered territory as wasteland or unproductive land, not forest
• forest dwelling, hunters/gatherers: will focus on areas that are diverse and provide them with food and resources; not “forests” planted by the foresters (not diverse, not a good source of food and resources)
• a qualitative epistemology: assumes that the reality of “forest” is shaped by human social factors; collect data (mental maps) but interpretation leads to ideas of how a forest is a human construct, even “untouched” forest
• a qualitative ontology that suggests that sensory measurements in the world are incapable of measuring and helping to understand the social construction of forest; “reality” is shaped and made via social processes.  Social theory explains social processes, but these are not “laws” or unchangeable.
• Counter Mapping: Peluso: Whose Woods Are These?

Data Models and Ontology

Vector data model: point, line, area (necessary to encode data into the GIS)

• People: US Census blocks
• define an area as a particular block, count the number of people
• all of space is filled with blocks
• what is the real nature of humans and where they live?

Raster data model: grid of cells

• land use: each cell (can be very fine) assigned a type of land use based on energy reflected from it
• complex mosaic of land uses often generalized into agricultural, commercial, etc
• again, all space is some kind of land use
• what is the real nature of land use?

Object oriented data models

• see all geographic features as objects; location as one attribute
• group together similar objects (roads) and have subclasses (federal, state, local)
• hierarchical: each “parent” object has attributes common to its subclasses
• discrete, separate entities in a neutral space; can fill space or not
• what is the real nature of any geographic feature?  “Forest” as an object?

All data models are reductionist: they simplify complex reality

Need to know how that is occurring and how it shapes understanding when using
GIS or any method

Looking for the Social in GIS

Social aspects of science, technology, GIS

• ex) funding and research on health biased towards white males

Science as autonomous vs social

Kuhn: The Structure of Scientific Revolutions

• paradigms: accepted practices and belief systems in science; structure how science is done until enough doubt is cast to accept a new system.
• what is to be observed and scrutinized, the kind of questions that are supposed to be asked and probed for answers in relation to this subject, how these questions are to be put, how the results of scientific investigations should be interpreted.
• ex) Copernicus proposed a cosmology with the Sun at the center and the Earth as one of the planets revolving around the Sun.

GIS technology: how have applications developed for the military and environmental science come to shape studies using GIS for non-military and non-environmental science applications?

• WWW-based GIS and open source GIS: socially driven

What is the point ?

GIS is growing rapidly as a method used by diverse people and for diverse applications use growing faster than an understanding of the assumptions and limits of GIS particularly more conceptual, theoretical, even philosophical issues

Important to approach and use GIS with a few things in mind

• it does tend to privilege one of many approaches to understanding the world: empirical, positivist, scientific; it is not the only way to address particular issues and is not “neutral” or “objective” or necessarily better than other approaches with different assumptions.
• it is connected to social context: it is a powerful, persuasive tool that has been developed for military and government applications; it is used by experts with training and organizations with big budgets and power; it is in many ways a very elitist method for understanding the world.
• GIS is always in flux: GIS is not set in stone: development of internet GIS: GoogleMaps and GoogleEarth and slew of similar applications: how will this more “populist” GIS open the door to different kinds of GIS and GIS analysis?  GIS technology will always evolve within a social context.

Schuurman GIS: A Short Introduction

Ch. 3: The Devil is in the Data: Collection, Representation, and Standardization

“Data are not the transparent manifestation of reality in digital terms.  They are the expression of particular points of view and agendas that begin as observations, and are transformed into numbers in data tables that provide the basis for spatial analysis.”

“Data are an artifact that reflects people, policy, and agendas.”

The Politics and Practicalities of Data Collection

Human data collection: often by area (Census block, zip code)

• U.S. Census: Politics of counting people
• Census count leads to allocation of money, voting districts
• undercount of homeless, poor, minorities (3.3 million in 2000)
• Pima Co. Az: 15,000 undercount, $30 million loss in funds
• Delaware Co. OH: undercount of about 8000
• statistical sampling can correct (opposed for Political reasons)

Environmental data collection: often by location

• GPS: relatively to very accurate locations
• Military origins; selective availability; competing system (Galileo, EU)
• primary (collect yourself) vs secondary (use already collected) data

Organizing Data

• table of data in GIS: like a spreadsheet: ArcGIS Demo
• spatial data: location (in some coordinate system): where
• attribute data: describe the spatial data: what
• consistency: should not be gaps or missing data (although common)
• scale: large vs small scale maps;
• scale does not exist in computers; generalize to view: ex) Google Maps
• scale at which data has been collected (detailed vs general): ex) DALIS data vs. ESRI data
• aggregation: group of Census blocks > Block group > Census Tract: ex) Geog 222 Exercise 6
• data interpolation: filling in missing values: terrain shading, temperature

Metadata: Data about Data

Sharing data leads to the need to know about data: when collected, at what scale, who…

• ex) DALIS data

Sharing Data: Interoperability

• “a common language for computational environments”
• cross-platform and cross-software data compatability
• like a text (.txt) file

Semantic interoperability: the practical problem associated with “philosophical” issues

• ex) pond: means different things to different people/institutions, thus different in different data sets: how to integrate?
• ex) wildlife biologists (forest classified in terms of habitat vs foresters (forest classified in terms of resource assessment)
• ex) different ways of defining what a road isuse metadata to assess these differences
• they will always exist: ignoring them can lead to problems

Moral of the story: data are not reality!

Conclusion

“Data are compiled with a particular purpose in mind, and they reflect the assumptions and preconceptions of both the data collectors and data users.  They are, in fact, stories about the world that change depending on the teller.”

• data is the basis of all GIS analysis
• not a matter of good or bad data
• not a matter of more or less accurate data
• but a matter of the appropriateness of the data to a particular task
• metadata clarifies the story the data can tell: who collected it with what assumptions under what conditions and for what purpose
• vital to be critical and understand your data, not just take it as a given

Mitchell: The ESRI Guide to GIS Analysis, ch. 1

GIS technology 30 years old

Good for making maps: but can do more than that: GIS Analysis

• learn new things, help make decisions
• maps result from GIS analysis: have important impact (visual)

Why GIS might not be used:

• lack of data (changing rapidly, but still a problem)
• difficult software (but now easy interfaces; still complex though)
• lack of understanding about GIS analysis (the book)

• where are things in geographic space?
• mapping variations in amount: least and most
• mapping density
• finding what is inside
• finding what is nearby
• mapping change

What is GIS Analysis?

GIS Analysis as a process

• simple visual analysis to complex digital modeling
• akin to the research process

1. Frame the Question:

• where are endangered ecosystems in Delaware County?
• where are potential recreational trail corridors in Delaware County?
• how can viable OWU food waste be efficiently distributed to area food banks?
• where does the food sold on campus come from, and what are the consequences of our consumption of these foods?
• who is your audience?  what is your final goal?

2. Understand your Data

• what is the context of your question?  who are the experts?  literature, people
• what do you have to know about the context of the question to answer it?

• what is an endangered ecosystem?  what are specific examples?
• what are the goals of recreational trails?  what do they connect?
• how is food waste reuse assessed and how is it collected?
  
• what or who can help you to understand the issue: literature, people

3. Choose a Method

• what data is available to help answer your question?  cost? compatibility?
• what data do you have to generate yourself? easy vs. difficult vs impossible

4. Process the Data: specific analysis

• ex) generate endangered areas by comparing areas defined as important ecosystems to their closeness to recent development
• ex) generate potential trails by generating important points and areas to connect; and determining feasible paths between those points; relate potential trails to property ownership and other factors
• ex) generate a plan for distributing food waste from campus to area food banks
• ex) analyze the global impact of specific food consumption on campus

5. Look at the Results

• generate a map (with a database) and use it to present results
• ex) map of endangered ecosystems in Delaware Co: distribute to ??
• ex) map of potential trails in Delaware Co.: planners, bike clubs, etc.
• ex) a map that guides distribution of OWU food waste
• ex) map of the global impact of what we eat

• vital part of the process: communication and advocacy

• Simple in concept; complex in application!

Understanding Geographic Features

• we reduce the complexity of the real world in order to collect data and map it

A feature: “something inherent and distinctive”

Types of features (mappable data)

1. Discrete Features: at any location, the feature is there or is not there

• point, line, and area example: p. 12
• corresponds to vector data structure in most GIS programs

2. Continuous Features: feature is everywhere in varying amounts

• ex) temperature
• ex) elevation
• ex) soil or bedrock (Delaware Data)

3) Features Summarized by Area: census or count data

• define an area; count features in the area; assign total to the area
• know how many features in an area, but not where they are in the area
  ex) US Census data, animal census

Two Ways of Representing Geographic Features

1) Vector: points, lines, and areas

• each point has a unique location in a coordinate system: latitude/longitude
• points connect to make lines
• series of points, connected to make lines, which close are areas

2) Raster: grid of varying resolution with cells

• air photo

Different data structures; can be related in GIS but generated differently and stored and processed differently.

Map Projections and Coordinate Systems

Review from Geog 222 or 353

• coordinate systems: based on the idea of a graph
• locations in geographic space: x, y
• latitude longitude vs state plane coordinate system
• coordinate layers of GIS information

• map projection
• 3D earth to 2D map
• distortions inherent in process (shape, area)
• distortions less evident at detailed scales
• but GIS layers must have same map projection or will not align properly

Understanding Geographic Attributes

• a geographic feature (point, line, area) has one or more attributes
• ex) area is a vernal pool, it is 1 acre, it is on private property (3 attributes)

Types of attribute values

• categories: qualitative
• ex) vernal pool (area) vs river (line)

Ranks: quantitative with order

• ex) water quality: high, medium, low

Counts and amounts: quantitative, total numbers

• ex) 35 robins in one nature reserve, 67 in a second reserve

Ratios: relationship between two quantities

• ex) people per household in census tracts in Delaware county

Data tables: the ‘database’ or spreadsheet where the feature attributes are found

• ex) select all properties in Delaware County that are residential land use
• ex) calculate and summarize the total value of all properties a proposed trail crosses

Course Project Ideas

• apply Mitchell’s process of GIS Analysis to your forming project

What to do for Next Time

• Student Presentations I (Newman & Shkoukani)
• Discuss & Blog: Schuurman ch. 2 & 3
• Continue to develop class project ideas

A Dangerous Road…

Geospatial Analysis – A Comprehensive Guide

Notes and examples on “Introduction & Terminology” and “Conceptual Frameworks for Spatial Analysis.”

Introduction & Terminology

1. On applications: London GIS Casebook: all related to “environmental justice.”

• Noise Mapping/GIS
• Air Quality
• Crime
• Health and Health

2. GIS, Spatial Analysis, and Software

• overview of GIS and related software (Table 1-1)
• choosing a GIS software package
• visualization, mapping, cartography

3. Terminology & Definitions

• massive array of jargon (Table 1-2)
• statistics & GIS

Conceptual Frameworks for Spatial Analysis

The Geospatial Perspective: “a distinct perspective on the world, a unique lens through which to examine events, patterns, and processes that operate on or near the surface of our planet.”

The domain of geospatial analysis is the surface of the Earth, extending upwards in the analysis of topography and the atmosphere, and downwards in the analysis of groundwater and geology. In scale it extends from the most local, when archaeologists record the locations of pieces of pottery to the nearest centimetre or property boundaries are surveyed to the nearest millimetre, to the global, in the analysis of sea surface temperatures or global warming. In time it extends backwards from the present into the analysis of historical population migrations, the discovery of patterns in archaeological sites, or the detailed mapping of the movement of continents, and into the future in attempts to predict the tracks of hurricanes, the melting of the Greenland ice-cap, or the likely growth of urban areas.

Geospatial Analysis: what happens where, and makes use of geographic information that links features and phenomena on the Earth’s surface to their locations.

1. Basic “Primitives”

• place: rather complicated: Wikipedia
• attributes: “any recorded characteristic or property of a place” + measurement levels + examples in ArcGIS
• objects: raster (images) & vector (points, lines, areas): here and below (from Making Maps):

• maps: defining maps and more defining maps (PDF)
• multiple properties of place: attributes & classification
• fields: discrete (example) and continuous (example) phenomena
• networks: example & example
• density: example & example
• detail: scale and generalization (from Making Maps):

• topology: here and here.

2. Spatial Relationships

• co-location: correlation of asbestos (vermiculite ore) & asbestosis, lung cancer and mesothelioma
• distance and direction: routing emergency vehicles (network analysis)
• spatial context: more or less the same as co-location
• neighborhood: defining a neighborhood in GIS and buffers
• spatial heterogeneity: “The results of any analysis over a limited area can be expected to change as that limited area is relocated, and to be different from the results that would be obtained for the surface of the Earth as a whole.”  In essence, places are complicated and prediction from place to place difficult.
• spatial dependence: even though places are complicated: Tobler’s “First Law of Geography”: “All things are related, but nearby things are more related than distant things.”  Example: Bike trails and property values
• spatial sampling: weather map and terrain:

• spatial interpolation: filling in between known data

• smoothing and sharpening (generalization; see above)

3. Spatial Statistics

• Spatial probability: probability of landslides
• Uncertainty: variation in how certain we are about what we analyze and map with GIS: soils and water quality
• Statistical inference: figure 2-5 and inferring who you are for marketing

4. Spatial Data Infrastructure

• Geoportals: available GIS data for Delaware Co. Ohio and the world.
• Metadata: NSDI

• Interoperability: standards for spatial data (so everything works together): OGC

…All this jargon…

Mapping Environmental Justice in Delaware County Pennsylvania

• Use as a model for a similar project in Delaware County Ohio
• What is missing?  Not applicable?
• Changes in emphasis and organization
• Personal interests of students in the class in regards to the different topics

Contents:

Preface

A note about the mapping process
Figure 1: Municipalities

Introduction: Environmental Justice

From Here to There: Transportation and Industrial History
Figure 2:  Transportation

Natural Setting: Green Spaces and Waterways
Figure 3:  Land Cover

Demographic Distributions: Income, Race, Education, Unemployment and Age
Figure 4:  Income
Figure 5:  Race
Figure 6:  Unemployment
Figure 7:  Education
Figure 8:  Age

Pollution Flow: Air and Water
Figure 9:  Air and Water Pollution

Current Waste Disposal Sites: Landfills and Incinerators
Figure 10:  Waste

Abandoned Waste: Superfund Sites and Inactive Landfills
Figure 11:   Abandoned Waste

Hot Spots: Top 15 polluters in the County
Figure 12:  Top Fifteen Polluters

Conclusion: What does this mean for Delaware County?

Appendix 1: Glossary
Appendix 2: Environmental Organizations in Delaware County
Notes…

Beware Fat Duck Surprises

1. Readings

Schuurman ch. 1, “Geography Matters”

Introducing the Identities of GIS

The Success of GIS: is it now what Geography is?  Ubiquitous technology & computing (example)

• technological advances, widespread adoption (research, business, gov, personal)
• people may not have heard of GIS but they use it and are affected by it
• GoogleMaps or Yahoo Maps or MapQuest are all GIS
• municipal, utilities management: Delware County Ohio: DALIS Project
• business and marketing: PRIZM data: you are where you live

“This book is designed to inform the reader about precisely how GIS affects them as well as myriad social processes” (1)

• a more human & social approach to technology, intellectual rather than only technological

The problem of GIS and geography: love/hate

• GIS as one way of understanding “geography” – but dominating at the moment
• quantitative vs. qualitative methods
• epistemology: The branch of philosophy that studies the nature of knowledge, its presuppositions and foundations, and its extent and validity.  How we know.

Identity of GIS: what is it?

Delware County Ohio: DALIS Project: a tool for storing complex data; practical problem solving

• where

Geography 222 “PsychoGeography” maps / Mental Maps

• how to collect qualitative & strange data and map it

Delaware Recreational Trails

• what is most important when locating a recreational trail?
• logic of quantitative methods for optimizing, or qualitative data used to anticipate how people will react (and why)?  Epistemological issues!
• Delaware Trails research paper (PDF)

Where Does GIS Come From?

• 1960s era technology and epistemology

McHarg and the GIS “overlay” method: locating a road: done manually

• encode in a computer: technology and a particular way of knowing
• what is not taken into account in this approach
• spatial analysis: a means of extracting information (knowledge) from data
• let a computer do what McHarg did
• maps allow us to see raw data, or interact with data as we are analyzing it, or show the results of what we did
• 1950s-60s: development of computational analysis and spatial analysis tools
• wed technology to methods of knowing

• Harvard Graphics Lab: research spurred development of GIS software (and ESRI)

The Messy Business of Digging for Roots: GIS’s Intellectual Antecedents

Examples of the Precursors of GIS: technology is easy, epistemology not

• ex) J.K. Wright: “The Terminology of Certain Map Symbols” (1944): point, line, area: 1930s for map symbolization; basis of “vector data”
• ex) certain kinds of data easier to collect and analyze and map, they seem more intuitive maybe because they are what we are used to doing.
• ex) Historians reluctance to use GIS: Historical GIS

What does GIS stand for?

• definitions describe technology (systems; application): GIS(systems) = GIS
• hard/software for data input, analysis, output
• “black box:” assume the methods in the software are legitimate, don’t question

• definitions describing methods and process (science; theory): GIS(cience) = GISci
• origin of the methods, critique of the methods, new methods
• conceptual models of geographic space, sphericity of the real world vs. flat world of GIS,
• uncertainty and error, analytical methodologies, cognitive aspects.
• also Participatory GIS, Critical Cartography & GIS: myriad of human/social issues
• justifying and shaping an intellectual/academic role in GIS
• myriad of issues of intellectual importance (that one may not think about at all if only approaching GIS as black box technology).
• epistemology (how we know) and ontology (what the world must be like in order to be known): the debate on evolution/science vs creationism/intelligent design
• does geography (and its concepts/theories) drive GIS, or does GIS drive geography?  Debates in the field.

Data in, Information Out: Common Ground between GISys and GISci

GISys and GISci hard to differentiate in practice

• ex) data classification: the categories we put things into
• ex) house: what defines what a house is?  Is an apartment a house?  A dorm?  A condo?  A long-term residential hotel?  The kind of issue both Sys and Sci people have to deal with
• ex) boundaries: complexity in drawing: neighborhood boundaries have to be drawn if you are using GIS, but where to draw them?  How do you define a neighborhood (which is a classification of place)
• visualization: using intuition and knowledge to see patterns and connections:
  different epistemological approach – visual, not analytical.
• Dr Snow example: Broad St. pump and cholera p. 15

Geography Matters

• a quadzillion GIS applications… and jobs

2. Introductions & Interesting GIS application (w/examples)

• Major, year, experience with mapping & GIS, hobbies, future goals, favorite goat breeds, etc.
  

3. Next Time

• see course schedule
• after class: blog clean-up and questions

On Pareidolia

Geography 355: a follow up to Geography 222 & 353

But no prereq!  Why?  some problems with this!
Best to take 222 then 353 then 355 but any combination OK

Different ways to teach this course:

Lectures + series of exercises
Lectures + one big exercise broken into parts (Geog 353)

Or get away from those formats:

Student presentations of readings
Self guided tutorial
Applied, real-world group project (practicum, service learning, etc.)

Exhibit A: GIS Texts for course and software (ArcGIS)

GIS: set of concepts and hardware and software
Data input, analysis, output
Capabilities and applications expanding exponentially

ex) Delaware Data in ArcGIS

Data Input (how?)
Data Layers (examples)
Data analysis (property owners along a particular trail)
Data output (printer)

Course goal: become familiar (or more familiar) with GIS concepts, functionality, software

Exhibit B: Delaware Recreational Trail and Green Spaces materials; Environmental Justice materials; SNC Projects (2008, Fall)

GIS is so popular because it is useful: many applications, but GIS applications are a lot of work!

Data input: where is data from?  format?  what data do you need?
Analysis: how analytical capabilities of GIS solve real problems?
Output: on computer screen?  paper?  WWW?  To what audience?

The complexities of an actual application

Knowing enough about an application so that you can use GIS: experts
The human context: working in a group, project politics, costs involved, institutions within which GIS is supposed to function

Course Goal: Learn that GIS is much more than a bunch of software functions in Arc GIS

The goal this semester is to bring together exhibits A and B

Learn about GIS as a software tool: its functions, capabilities
Apply what we learn to a real world project

In working through a real world application we will learn what GIS is really about much more than just software and hardware

Geography 353: Scripted project, all figured out for you, me active, you more passive

Useful for learning…but limited…just following instructions

This course: active learning for all of us

We will work as a group (or in sub groups) throughout the semester
You will be active in shaping what we do and how we do it
The success of the course depends on your engagement in the course
You will push yourself and me to get the most you can get out of this course

Problems: anxiety provoking, potential for disorder and problems, unmotivated & passive students

Benefits: learn a lot in “real world” setting with real problems to solve, forced to move beyond passive lump in class, maybe even have an impact

Field trip! (ok, carpet trip)

Delaware Trails Project

OWU students: smart, motivated, engaged; and small class sizes

Upper level courses should involve real engagement (so that is what I expect)

Bottom Line: for this course to work:

Active participation by all students: lumpen passivity not allowed

Collaboration with each other and OWU and community folks

Students should expect to play an active and vital role in the class and in the project!

If you don’t like that kind of class, then drop!

Review: Syllabus and Schedule and General Course Structure (blog)

Create your Course Blog

1) go to wordpress.com

2) sign up and create a blog

3) set up the look of the blog and create some categories

Class Readings

Class Project

Class Exercises

Evaluations

Personal

4) new post: introduction to you

5) new post: Schurmann reading (ch. 1) notes, comments, questions

6) new post: One GIS application area of interest, with at least 3 links & graphics

7) email me the URL to your Blog by 10am Wednesday: thus all the stuff above is due then.